Machine learning device, laser machine, and laser machining system

ABSTRACT

A machine learning device performs machine learning on a laser machine including a plurality of galvanometer mirrors for reflection of a laser beam and a plurality of galvanometer motors for driving the galvanometer mirrors to rotate, and scanning the laser beam over a workpiece. The machine learning device includes: input data acquisition unit that acquires at least two detected temperatures from the galvanometer mirrors and the galvanometer motors as input data; label acquisition unit that acquires a coefficient as a label for calculating a machining target position from an actual position of machining with the laser beam on the workpiece; and learning unit that performs supervised learning using a set of the label and the input data as training data to construct a mathematical model for calculating the machining target position from the actual machining position on the workpiece based on the at least two detected temperatures.

This application is based on and claims the benefit of priority fromJapanese Patent Application No. 2019-032802, filed on 26 Feb. 2019, thecontent of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to a machine learning device, a lasermachine, and a laser machining system.

Related Art

There has been a laser machine conventionally known that performsmachining by scanning a laser beam over a workpiece in a predetermineddirection. For example, patent document 1 discloses a laser machine thatperforms machining with a laser beam deflected with a galvanometermirror and focused with an fθ lens. To avoid reduction in machiningposition accuracy to be caused by change in temperature at thegalvanometer mirror or a galvanometer scanner, this laser machineincludes: galvanometer temperature detection means for detecting atemperature at the galvanometer mirror; lens temperature detection meansfor detecting a temperature at the fθ lens; and means for controlling adeflection and displacement motion position of the galvanometer mirroron the basis of the temperatures from the galvanometer temperaturedetection means and the lens temperature detection means.

Patent document 2 discloses a controller for a galvanometer scannerincluding control means for operating an actuator for a mirror on thebasis of deviation between a target value and a current value andcontrolling an output angle of a beam input to the mirror. To allowimplementation of machining with excellent accuracy even on theoccurrence of thermal deformation of an oscillation axis, thiscontroller for the galvanometer scanner includes: means for storing arelationship between a current value to be supplied to the actuator anda deviation value; and means for estimating the deviation value from thecurrent value to be supplied to the actuator. The control meansestimates the deviation value on the basis of the current value duringmachining, and compensates for the target value so as to cancel theestimated deviation value.

Patent document 3 discloses a laser machine that performs lasermachining on a machining target while controlling a galvanometerreflection mirror and controlling a machining position on the machiningtarget. To compensate for position error of the laser machiningcorrectly and allow high-accurate machining on the machining targetusing a simple configuration, this laser machine includes: a positioncommand calculation unit that compensates for a machining targetcoordinate in coincidence with predetermined timing on the basis of anoffset compensation coefficient for compensating for the machiningtarget coordinate calculated on the basis of position error occurringduring machining, compensates for the machining target coordinate incoincidence with predetermined timing on the basis of a temperaturecompensation coefficient for compensating for the machining targetcoordinate calculated on the basis of a temperature at the galvanometerreflection mirror and the position error occurring during machining, andoutputs results of the compensation as position command information; anda galvanometer mirror control unit that controls the galvanometerreflection mirror on the basis of the position command information.

Patent Document 1: Japanese Unexamined Patent Application, PublicationNo. 2003-290944

Patent Document 2: Japanese Unexamined Patent Application, PublicationNo. 2005-292322

Patent Document 3: Japanese Unexamined Patent Application, PublicationNo. 2007-21507

SUMMARY OF THE INVENTION

Regarding compensation of position error of a laser beam from a targetposition in a laser machine, while the position error is changed bythermal deformation, etc. occurring in response to change in temperatureat a galvanometer mirror during machining, the position error isaffected by a plurality of optical parts and constituting members. Thishas caused difficulty in determining a compensation value in response tothe temperature change.

(1) A first aspect of this disclosure is intended for a machine learningdevice that performs machine learning on a laser machine comprising aplurality of galvanometer mirrors for reflection of a laser beam and aplurality of galvanometer motors for driving corresponding ones of thegalvanometer mirrors to rotate, and scanning the laser beam over aworkpiece, The machine learning device comprises: input data acquisitionunit that acquires at least two detected temperatures from thegalvanometer mirrors and the galvanometer motors as input data; labelacquisition unit that acquires a coefficient as a label for calculatinga target position of machining with the laser beam on the workpiece froman actual machining position relative to the machining target position;and learning unit that performs supervised learning using a set of thelabel and the input data as training data to construct a mathematicalmodel for calculating the machining target position from the actualmachining position on the workpiece on the basis of the at least twodetected temperatures.

(2) A second aspect of this disclosure is intended for a laser machinecomprising: the machine learning device described in; a scanner headcomprising a plurality of galvanometer mirrors for reflection of a laserbeam and a plurality of galvanometer motors for driving correspondingones of the galvanometer mirrors to rotate, and scanning the laser beamover a workpiece; position command calculation unit that calculates amachining target position on the workpiece; position detection unit thatdetects an actual machining position relative to the machining targetposition on the workpiece; coefficient calculation unit that calculatesa coefficient for calculating the machining target position output fromthe position command calculation unit from the actual machining positionoutput from the position detection unit; at least two temperaturedetection unit that detect at least two detected temperatures from thegalvanometer mirrors and the galvanometer motors; and compensatedposition command calculation unit that calculates a compensatedmachining target position for making a match between the machiningtarget position and the actual machining position from the machiningtarget position on the workpiece at the at least two detectedtemperatures detected by the at least two temperature detection unit byusing a mathematical model for calculating the machining target positionfrom the actual machining position relative to the machining targetposition on the workpiece on the basis of the at least two detectedtemperatures output from the machine learning device.

(3) A third aspect of this disclosure is intended for a laser machinecomprising: a scanner head comprising a plurality of galvanometermirrors for reflection of a laser beam and a plurality of galvanometermotors for driving corresponding ones of the galvanometer mirrors torotate, and scanning the laser beam over a workpiece; position commandcalculation unit that calculates a machining target position on theworkpiece; at least two temperature detection unit that detect at leasttwo detected temperatures from the galvanometer mirrors and thegalvanometer motors; and compensated position command calculation unitthat calculates a compensated machining target position for making amatch between the machining target position and an actual machiningposition from the machining target position on the workpiece at the atleast two detected temperatures detected by the at least two temperaturedetection unit by using a mathematical model acquired through supervisedlearning using the at least two detected temperatures as input data anda coefficient as a label for calculating the machining target positionfrom the actual machining position relative to the target position ofmachining with the laser beam on the workpiece.

(4) A fourth aspect of this disclosure is intended for a laser machiningsystem comprising the machine learning device described in and a lasermachine. The laser machine comprises: a scanner head comprising aplurality of galvanometer mirrors for reflection of a laser beam and aplurality of galvanometer motors for driving corresponding ones of thegalvanometer mirrors to rotate, and scanning the laser beam over aworkpiece; position command calculation unit that calculates a machiningtarget position on the workpiece; position detection unit that detectsan actual machining position relative to the machining target positionon the workpiece; coefficient calculation unit that calculates acoefficient for calculating the machining target position output fromthe position command calculation unit from the actual machining positionoutput from the position detection unit; at least two temperaturedetection unit that detect at least two detected temperatures from thegalvanometer mirrors and the galvanometer motors; and compensatedposition command calculation unit that calculates a compensatedmachining target position for making a match between the machiningtarget position and the actual machining position from the machiningtarget position on the workpiece at the at least two detectedtemperatures detected by the at least two temperature detection unit byusing a mathematical model for calculating the machining target positionfrom the actual machining position relative to the machining targetposition on the workpiece on the basis of the at least two detectedtemperatures output from the machine learning device.

According to each of the foregoing aspects, position error of a laserbeam from a target position in a laser machine can be compensated for inresponse to temperature change while the compensation is to be affectedby a plurality of optical parts and constituting members.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing the configuration of a laser machineaccording to an embodiment of this disclosure during machine learning;

FIG. 2 is a block diagram showing the configuration of the laser machineaccording to the embodiment of this disclosure after machine learning;

FIG. 3 shows a part of an optical system of a scanner head, a CCDcamera, and a temperature sensor in the laser machine;

FIG. 4 shows the entire configuration of the optical system of thescanner head;

FIG. 5 shows a scanning route of a laser beam output onto a workpieceaccording to a learning program;

FIG. 6 shows machining target positional data (x, y) beforecompensation;

FIG. 7 shows a determinant corresponding to a system consisting ofmathematical expressions 1 for calculating a coefficient a_(ij) and acoefficient b_(ij);

FIG. 8 is an explanatory view showing a relationship between a commandposition and an actual irradiation position before compensation and arelationship between a command position and an actual irradiationposition after the compensation;

FIG. 9 is a block diagram showing the configuration of a machinelearning unit;

FIG. 10 is an explanatory view showing the configuration of a learningunit determined by deep learning;

FIG. 11 is a flowchart showing operation relating to machine learning bythe machine learning unit according to the embodiment; and

FIG. 12 is a block diagram showing the configuration of a lasermachining system according to an embodiment of this disclosure.

DETAILED DESCRIPTION OF THE INVENTION

An embodiment of this disclosure will be described below by referring tothe drawings.

Entire Configuration of Laser Machine

FIG. 1 is a block diagram showing the configuration of a laser machineaccording to an embodiment of this disclosure during machine learning.FIG. 2 is a block diagram showing the configuration of the laser machineaccording to the embodiment of this disclosure after machine learning.FIG. 3 shows a part of an optical system of a scanner head, a CCDcamera, and a temperature sensor in the laser machine. In FIGS. 1 and 2, a laser machine 10 includes a control unit 100, a laser oscillator200, a scanner head 300, a CCD camera 400, a temperature sensor 500, acoefficient calculation unit 600, and a machine learning unit 700. Thecoefficient calculation unit 600 may be incorporated into the machinelearning unit 700.

The control unit 100 includes a program analysis unit 101, a lasercommand output unit 102, a position command calculation unit 103, a headcontrol unit 104, and a compensated position command calculation unit105. The compensated position command calculation unit 105 may beincorporated into the position command calculation unit 103. In theembodiment, during machine learning, while the compensated positioncommand calculation unit 105 is not operated, the control unit 100, thelaser oscillator 200, the scanner head 300, the CCD camera 400, thetemperature sensor 500, the coefficient calculation unit 600, and themachine learning unit 700 are operated. The laser machine 10 does notexert control for suppressing position error of an actual machiningposition from a machining target position defined in a machining programfor machine learning. Thus, in FIG. 1 , machining target positional datafrom the position command calculation unit 103 is not input to thecompensated position command calculation unit 105, and a detectedtemperature from the temperature sensor 500 is not input to thecompensated position command calculation unit 105. For this reason, inFIG. 1 , a route between the position command calculation unit 103 andthe compensated position command calculation unit 105 and a routebetween the temperature sensor 500 and the compensated position commandcalculation unit 105 are indicated by dashed lines showing that theseroutes are disabled.

By contrast, in the embodiment, while the CCD camera 400, thecoefficient calculation unit 600, and the machine learning unit 700 arenot operated after machine learning, the control unit 100, the laseroscillator 200, the scanner head 300, and the temperature sensor 500 areoperated. The laser machine 10 uses the compensated position commandcalculation unit 105 to exert control for suppressing position error ofan actual machining position from a machining target position defined inan actual machining program. Thus, in FIG. 2 , an output from the CCDcamera 400 is not input to the coefficient calculation unit 600, and anoutput from the position command calculation unit 103 is not input tothe coefficient calculation unit 600. Further, an output from thecoefficient calculation unit 600 is not input to the machine learningunit 700, and a detected temperature from the temperature sensor 500 isnot input to the machine learning unit 700. For this reason, in FIG. 2 ,a route between the coefficient calculation unit 600 and the CCD camera400, a route between the coefficient calculation unit 600 and theposition command calculation unit 103, a route between the machinelearning unit 700 and the coefficient calculation unit 600, and a routebetween the machine learning unit 700 and the temperature sensor 500 areindicated by dashed lines showing that these routes are disabled. Afterthe machine learning, the CCD camera 400, the coefficient calculationunit 600, and the machine learning unit 700 may be separated from thelaser machine and the laser machine may be configured as a machinewithout the CCD camera 400, the coefficient calculation unit 600, andthe machine learning unit 700.

Each of the routes may be disabled by preventing signal output from asignal output side of each route, preventing acceptance of a signal by asignal input side of each route, or switching between transmission andinterruption of a signal using a switch provided in each route, forexample.

The program analysis unit 101 receives an actual machining programduring actual machining after machine learning or a machining programfor machine learning during the machine learning (hereinafter called alearning program) from an input device not shown, and analyzes theactual machining program or learning program. The program analysis unit101 analyzes the actual machining program or learning program, generateslaser output information about a laser beam L to be output from thescanner head 300 on the basis of a result of the analysis, and outputsthe generated laser output information to the laser command output unit102. Further, the program analysis unit 101 generates operation commandinformation about a direction of scanning of a laser beam with thescanner head 300 and a target speed of the scanning, and outputs thegenerated operation command information to the position commandcalculation unit 103.

The laser command output unit 102 outputs a laser output command to thelaser oscillator 200 in such a manner that the laser beam L output fromthe scanner head 300 produces intended laser output based on the laseroutput information output from the program analysis unit 101.

The laser oscillator 200 is composed of a laser medium, an opticalresonator, an excitation source, etc. (all of which are not shown). Thelaser oscillator 200 generates the laser beam L to produce laser outputbased on the laser output command, and outputs the generated laser beamL to the scanner head 300.

As an example, the scanner head 300 is a galvanometer scanner capable ofreceiving the laser beam L output from the laser oscillator 200 andscanning the laser beam L over a workpiece 20. As shown in FIG. 3 , thescanner head 300 includes two galvanometer mirrors 301 and 302 forreflection of the laser beam L output from the laser oscillator 200,galvanometer motors 311 and 312 for driving the galvanometer mirrors 301and 302 to rotate respectively, and a cover glass 320. As shown in FIG.4 , the scanner head 300 includes three focusing lens 330, 340 and 350,and a reflection mirror 360.

The cover glass 320 has a circular columnar shape. The cover glass 320has the function of causing the laser beam L to pass through after thelaser beam L is sequentially reflected on the galvanometer mirrors 301and 302, and protecting the interior of the scanner head 300.

During machine learning, the position command calculation unit 103generates machining target positional data on the basis of the operationcommand information, and outputs the machining target positional data asa position command to the head control unit 104 and the coefficientcalculation unit 600. The position command calculation unit 103corresponds to position command calculation means. After the machinelearning, the position command calculation unit 103 outputs themachining target positional data to the compensated position commandcalculation unit 105, and outputs compensated machining targetpositional data as a position command generated by the compensatedposition command calculation unit 105 to the head control unit 104. Thecompensated machining target positional data may be output directly fromthe compensated position command calculation unit 105 to the headcontrol unit 104 without intervention by the position commandcalculation unit 103.

The head control unit 104 outputs the position command from the positioncommand calculation unit 103 to the scanner head 300 as driving controldata for rotating the galvanometer motors 311 and 312. On the basis ofthe driving control data, the galvanometer motors 311 and 312 of thescanner head 300 rotate the galvanometer mirrors 301 and 302respectively and independently of each other about two rotary axes J1and J2 perpendicular to each other, thereby controlling scanning of thelaser beam L output from the scanner head 300 to the workpiece 20.

A scanning route of the laser beam L output from the scanner head 300onto the workpiece 20 can be changed optionally in an X direction and aY direction shown in FIG. 5 by controlling rotary driving of thegalvanometer motors 311 and 312 appropriately and changing therespective rotation angles of the galvanometer mirrors 301 and 302. FIG.5 shows a scanning route of the laser beam output onto the workpieceaccording to the learning program. As shown in FIG. 5 , the scanningroute of the laser beam has a lattice pattern. The shape of the scanningroute of the laser beam is not particularly limited to a lattice patternbut may be a different pattern.

The CCD camera 400 captures an image of a machining position on theworkpiece 20, determines machining positional data (x′, y′) from anoutput image of a machining locus, and outputs the determined machiningpositional data (x′, y′) to the coefficient calculation unit 600. TheCCD camera 400 corresponds to position detection unit. The temperaturesensor 500 measures temperatures at the galvanometer mirrors 301 and 302and the galvanometer motors 311 and 312 to affect laser beam scanning,and outputs the measured temperatures to the machine learning unit 700during machine learning and to the compensated position commandcalculation unit 105 after the machine learning. For example, thetemperature sensor 500 is non-contact type temperature sensors 500 a 1and 500 b 1 provided adjacent to the galvanometer mirrors 301 and 302respectively, and contact type temperature sensors 500 a 2 and 500 b 2attached to the galvanometer motors 311 and 312 respectively. Thetemperature sensor 500 corresponds to temperature detection unit.

Position error of an actual machining position from a machining targetposition is caused by thermal deformations of the galvanometer mirrors301 and 302, distortion of assembly resulting from thermal deformationsof fixed parts of the galvanometer mirrors 301 and 302, and distortionof assembly resulting from thermal deformations of fixed parts of thegalvanometer motors 311 and 312, for example.

The position error of the machining position from the machining targetposition is also caused by thermal deformations of the three focusinglenses 330, 340 and 350 and the reflector 360, and distortion ofassembly resulting from thermal deformations of fixed parts of the threefocusing lenses 330, 340 and 350 and a fixed part of the reflector 360.The position error of the machining position from the machining targetposition is caused by factors such as heat generated by a laser beam,heat generated by a galvanometer motor, and change in environmentaltemperature, for example. In the example described in the embodiment,temperature changes at the galvanometer mirrors 301 and 302 are measuredusing the non-contact type temperature sensors 500 a 1 and 500 b 1, andtemperature changes at the galvanometer motors 311 and 312 are measuredusing the contact type temperature sensors 500 a 2 and 500 b 2. Ifconsideration is further given to temperature changes at the threefocusing lenses 330, 340 and 350 and the reflection mirror 360 fortemperature compensation in the laser machine, a non-contact type orcontact type temperature sensor is attached to each of the threefocusing lenses 330, 340 and 350 and the reflection mirror 360.

The coefficient calculation unit 600 acquires machining targetpositional data (x, y) before compensation from the position commandcalculation unit 103. Further, the coefficient calculation unit 600acquires the machining positional data (x′, y′) from the CCD camera 400.The CCD camera 400 may perform process of outputting an output image,and the coefficient calculation unit 600 may perform process ofdetermining the machining positional data (x′, y′) from the outputimage. In this case, the CCD camera 400 and part of the coefficientcalculation unit 600 correspond to the position detection unit. Then,the coefficient calculation unit 600 calculates an M² point by solvingmathematical expressions 1 (also called Math. 1) simultaneously. Thecoefficient calculation unit 600 corresponds to coefficient calculationmeans.

$\begin{matrix}{{x = {\sum\limits_{i = 0}^{N}{\sum\limits_{j = 0}^{N}{a_{ij}x^{\prime i}y^{\prime\; j}}}}}{y = {\sum\limits_{i = 0}^{N}{\sum\limits_{j = 0}^{N}{b_{ij}x^{\prime i}y^{\prime\; j}}}}}} & \left\lbrack {{Math}.\mspace{14mu} 1} \right\rbrack\end{matrix}$FIG. 6 shows a measurement point at the M² point. FIG. 7 shows adeterminant corresponding to a system consisting of the mathematicalexpressions 1 for calculating the coefficients a_(ij) and b_(ij) usingthe machining positional data (x′, y′) and the machining targetpositional data (x, y) about the machining point at the M² point. Thecoefficients a_(ij) and b_(ij) are calculated by simultaneously solvingthe Math. 1 as shown in FIG. 7 into which the machining positional data(x′, y′) and the machining target positional data (x, y) about themachining point at the M² point (M²≥(N+1)²) are substituted.

The machine learning unit 700 learns a coefficient a′_(ij)(T) and acoefficient b′_(ij)(T) about a detected temperature T from thetemperature sensor 500 defined in a mathematical model of themathematical expressions 2 (also called Math. 2) by using the detectedtemperature T as input data and the coefficients a_(ij) and b_(ij)output from the coefficient calculation unit 600 as a label. Then, themachine learning unit 700 outputs the mathematical model of themathematical expressions 2 to the compensated position commandcalculation unit 105. The machine learning unit 700 corresponds to amachine learning device. The mathematical model of the mathematicalexpressions 2 containing the coefficients a′_(ij)(T) and b′_(ij)(T)defined on the basis of the detected temperature T is a determinantformulated by replacing the coefficients a_(ij) and b_(ij) in FIG. 7with the coefficients a′_(ij)(T) and b′_(ij)(T) respectively.

$\begin{matrix}{{x = {\sum\limits_{i = 0}^{N}{\sum\limits_{j = 0}^{N}{{a_{ij}^{\prime}(T)}x^{\prime i}y^{\prime\; j}}}}}{y = {\sum\limits_{i = 0}^{N}{\sum\limits_{j = 0}^{N}{{b_{ij}^{\prime}(T)}x^{\prime i}y^{\prime\; j}}}}}} & \left\lbrack {{Math}.\mspace{14mu} 2} \right\rbrack\end{matrix}$The detected temperature T from the temperature sensor 500 correspondsto detected temperatures from the non-contact type temperature sensors500 a 1 and 500 b 1 and from the contact type temperature sensors 500 a2 and 500 b 2.

After machine learning, the compensated position command calculationunit 105 compensates for the machining target positional data (x, y)output from the position command calculation unit 103 by using themathematical model of the mathematical expressions 2 containing thecoefficients a′_(ij)(T) and b′_(ij)(T) output from the machine learningunit 700 and on the basis of the detected temperature from thetemperature sensor 500, and outputs machining target positional data(x″, y″) resulting from the compensation to the position commandcalculation unit 103. The compensated position command calculation unit105 corresponds to compensated position command calculation unit. Thecompensated machining target positional data (x″, y″) can be determinedby using a mathematical model of mathematical expressions 3 (also calledMath. 3) containing x″ and y″ as replacements for x and y respectivelyin the mathematical expressions 2 and containing x and y as replacementsfor x′ and y′ respectively in the mathematical expressions 2.

$\begin{matrix}{{x^{''} = {\sum\limits_{i = 0}^{N}{\sum\limits_{j = 0}^{N}{{a_{ij}^{\prime}(T)}x^{i}y^{j}}}}}{y^{''} = {\sum\limits_{i = 0}^{N}{\sum\limits_{j = 0}^{N}{{b_{ij}^{\prime}(T)}x^{i}y^{\; j}}}}}} & \left\lbrack {{Math}.\mspace{14mu} 3} \right\rbrack\end{matrix}$

The position command calculation unit 103 outputs the machining targetpositional data (x″, y″) as a position command to the head control unit104. The head control unit 104 drives the scanner head 300.

FIG. 8 is an explanatory view showing a relationship between a commandposition and an actual irradiation position (machining position) beforecompensation and a relationship between a command position and an actualirradiation position after the compensation. During machine learning,the head control unit 104 scans the scanner head 300 using the machiningtarget positional data (x, y) as a command position. The coefficientcalculation unit 600 acquires an output image of a machining locus fromthe CCD camera 400 obtained by scanning of a laser beam over theworkpiece 20, and determines the machining positional data (x′, y′)indicating an actual irradiation position. In the illustration of FIG. 8, the machining positional data (x′, y′) contains position error fromthe machining target positional data (x, y) occurring in such a mannerthat the absolute value of an x coordinate in the x direction isincreased and position error from the machining target positional data(x, y) occurring in such a manner that the absolute value of a ycoordinate in the y direction is reduced.

The machine learning unit 700 learns the coefficients a′_(ij)(T) andb′_(ij)(T) defined on the basis of the detected temperature T andcontained in the mathematical model of the mathematical expressions 2assuming the machining positional data (x′, y′) as the machining targetpositional data (x, y). After the machine learning, the compensatedposition command calculation unit 105 compensates for the machiningtarget positional data (x, y) output from the position commandcalculation unit 103 by using the mathematical model expressed by themathematical expressions 3 and on the basis of the detected temperaturefrom the temperature sensor 500, thereby acquiring the compensatedmachining target positional data (x″, y″).

The position command calculation unit 103 outputs the machining targetpositional data (x″, y″) as a position command to the head control unit104. The head control unit 104 scans the scanner head 300 on the basisof the machining target positional data (x″, y″) as a command position.An actual irradiation position (machining position) determined by thescanning of the scanner head 300 corresponds to the machining targetpositional data (x, y). In this way, it becomes possible to obtain alaser machine in which position error of an actual machining positionfrom a machining target position is suppressed.

The configuration and operation of the machine learning unit 700 as amachine learning device will be described further using FIG. 9 . FIG. 9is a block diagram showing the configuration of the machine learningunit. As shown in FIG. 9 , the machine learning unit 700 includes aninput data acquisition unit 710 as input data acquisition means, a labelacquisition unit 720 as label acquisition means, a learning unit 730 aslearning means, and a mathematical model storage unit 740.

The input data acquisition unit 710 acquires the detected temperature Tfrom the temperature sensor 500, and outputs the acquired input data tothe learning unit 730. The detected temperature T from the temperaturesensor 500 mentioned herein corresponds to detected temperatures fromthe non-contact type temperature sensors 500 a 1 and 500 b 1 and fromthe contact type temperature sensors 500 a 2 and 500 b 2. The labelacquisition unit 720 acquires the coefficients a_(ij) and b_(ij) fromthe coefficient calculation unit 600, and outputs the acquired label tothe learning unit 730.

By doing so, the detected temperature T as input data and thecoefficients a_(ij) and b_(ij) as a label are input as a set to thelearning unit 730. This set of the input data and the label correspondsto training data in machine learning.

The learning unit 730 performs supervised learning on the basis of thetraining data to calculate the coefficients a′_(ij)(T) and b′_(ij)(T)defined on the basis of the detected temperature T. The supervisedlearning itself is well known to a person skilled in the art, so that itwill not be described in detail but will be described in outline.

The supervised learning is performed using a neural network constructedby combining perceptrons, for example. More specifically, a set of thedetected temperature T as input data and the coefficients a_(ij) andb_(ij) as a label contained in the training data is given to the neuralnetwork, and learning is performed repeatedly while a weight added toeach perceptron is changed so as to match an output from the neuralnetwork with the label. For example, process employingforward-propagation and then back-propagation (also called backwardpropagation of errors) is performed repeatedly, thereby adjusting aweight value so as to reduce error between outputs from thecorresponding perceptrons.

The neural network used for the learning by the learning unit 730 mayhave three layers or more layers. The learning may be performed by meansof what is called deep learning (also called deep-structured learning).FIG. 10 is an explanatory view showing the configuration of the learningunit determined by the deep learning. In FIG. 10 , the neural network iscomposed of an input layer, an intermediate layer, and an output layer.This intermediate layer is composed of three layers. Temperatures T₀ toT_(L) in FIG. 10 indicate the detected temperature T as an input to theneural network. Coefficients a₀₀ to a_(NN) mean coefficients calculatedthrough the neural network. These temperatures T₀ to T_(L) includetemperatures T₀ to T₃ (L=3). The temperatures T₀ to T₃ correspond todetected temperatures from the non-contact type temperature sensors 500a 1 and 500 b 1 and from the contact type temperature sensors 500 a 2and 500 b 2. As described above, the features of the training data arelearned to acquire the coefficients a′_(ij)(T) and b′_(ij)(T)recursively for estimating an output from an input.

As described above, the supervised learning mentioned herein is toeliminate error between a label and output data by changing a weightvalue. In the embodiment, the label corresponds to the coefficientsa_(ij) and b_(ij) and an input corresponds to the detected temperatureT. As a result, the coefficients a′_(ij)(T) and b′_(ij)(T) calculated byrepeated implementations of the learning by the learning unit 730 becomethe coefficients in the mathematical expressions 2 used for determiningthe machining target positional data (x, y) from the machiningpositional data (x′, y′).

The mathematical model of the mathematical expressions 2 containing thecoefficients a′_(ij)(T) and b′_(ij)(T) calculated by the learning unit730 is output to the mathematical model storage unit 740.

The mathematical model storage unit 740 is a storage unit that storesthe mathematical model of the mathematical expressions 2 containing thecoefficients a′_(ij)(T) and b′_(ij)(T) calculated by the learning unit730. The compensated position command calculation unit 105 acquires themathematical model of the mathematical expressions 2 from themathematical model storage unit 740 before implementation of actualmachining, and constructs a mathematical model of the mathematicalexpressions 3 using the mathematical model of the mathematicalexpressions 2. The mathematical model of the mathematical expressions 3is for acquiring the compensated machining target positional data (x″,y″) from the machining target positional data (x, y). The machiningtarget positional data (x″, y″) takes a value resulting fromcompensation of actual position error occurring in response to thedetected temperature T from the machining target positional data (x, y).An actual irradiation position (machining position) acquired throughscanning of the scanner head 300 based on the acquired machining targetpositional data (x″, y″) corresponds to the machining target positionaldata (x, y).

The functional blocks of the machine learning unit 700 are as describedabove. To realize these functional blocks, the machine learning unit 700includes an operational processor such as a central processing unit(CPU). The machine learning unit 700 further includes an auxiliarystorage unit such as a hard disk drive (HDD) storing various types ofcontrol programs and a main storage unit such as a random access memory(RAM) for storing data temporarily required in execution of a program bythe operational processor.

In the machine learning unit 700, the operational processor reads anapplication and an OS from the auxiliary storage unit. While the readapplication and OS are developed on the main storage unit, operationprocess based on these application and OS are performed. On the basis ofa result of the operation, each type of hardware provided at each unitis controlled. By doing so, the functional blocks of the machinelearning unit 700 according to the embodiment are realized. Namely, theembodiment can be realized by cooperative work between hardware andsoftware.

As a specific example, the machine learning unit 700 can be realizedusing part of a personal computer, a server, or a computerized numericalcontrol (CNC) device. In consideration of a large amount of operationaccompanying machine learning by the machine learning unit 700,high-speed processing may be encouraged by installing graphicsprocessing units (GPUs) on a personal computer, and using the GPUs foroperation accompanying the machine learning by means of a techniquecalled general-purpose computing on graphics processing units (GPGPUs),for example. Additionally, for higher speed processing, two or morecomputers equipped with such GPUs may be used to construct a computercluster, and parallel processing may be performed using two or morecomputers included in this computer cluster.

Operation relating to machine learning by the machine learning unit 700according to the embodiment will be described next by referring to theflowchart in FIG. 11 . In step S11, the machine learning unit 700determines whether machine learning is finished.

Here, if the machine learning is finished, a determination is Yes instep S11 and the processing is finished. If the machine learning is notfinished, a determination is No in step S11 and the processing proceedsto step S12. In step S12 and step S13, the input data acquisition unit710 and the label acquisition unit 720 of the machine learning unit 700acquire input data and a label respectively. The specifics of theacquired pieces of data are the same as those described above.

In step S14, the learning unit 730 of the machine learning unit 700performs the machine learning using the training data. The specifics ofthe machine learning using the training data are also the same as thosedescribed above.

In step S15, the learning unit 730 outputs a learning model containingthe calculated coefficients a′_(ij) and b′_(ij) to the mathematicalmodel storage unit 740, thereby updating the learning model. Then, theprocessing proceeds again to step S11.

As described above, the processes from step S11 to step S15 are repeateduntil the machine learning is finished to continuously perform thelearning. The machine learning may be finished in response toinstruction from a user. Alternatively, the machine learning may befinished after being performed repeatedly a predetermined number oftimes, for example. As a result, it becomes possible to construct amathematical model for suppressing position error of an actual machiningposition from a machining target position.

The scanner head in the laser machine according to the embodiment mayhave three or more galvanometer mirrors to be rotated by correspondinggalvanometer motors independent of each other.

While the foregoing embodiments are preferred embodiments of the presentinvention, the scope of the present invention is not limited only toeach of the foregoing embodiments but the present invention can beimplemented as embodiments to which various changes are made within arange not deviating from the substance of the present invention. Forexample, the present invention can be implemented as embodiments towhich the following changes are made.

<Modification in which Control Unit Includes at least One of CoefficientCalculation Unit and Machine Learning Unit>

In the foregoing embodiment, the coefficient calculation unit 600 andthe machine learning unit 700 are configured as units separate from thecontrol unit 100. Alternatively, part or all of the function of thecoefficient calculation unit 600 may be realized by the control unit100. Still alternatively, part or all of the function of the machinelearning unit 700 may be realized by the control unit 100.

<Flexibility of System Configuration>

In the foregoing embodiment, the machine learning unit 700 is connectedto the control unit 100, the temperature sensor 500, and the coefficientcalculation unit 600 to form the laser machine. However, this is not thelimited configuration of the embodiment. FIG. 12 is a block diagramshowing the configuration of a laser machining system according to anembodiment of this disclosure. As shown in FIG. 12 , a laser machiningsystem 30 includes n laser machining units 10A-1 to 10A-n, n machinelearning units 700-1 to 700-n, and a network 800. Here, n is an optionalnatural number. The n machine learning units 700-1 to 700-n correspondto the machine learning unit 700 shown in FIGS. 1 and 2 . Each of thelaser machining units 10A-1 to 10A-n corresponds to the laser machine 10in FIGS. 1 and 2 from which the machine learning unit 700 is omitted.

The laser machining unit 10A-1 and the machine learning unit 700-1 areconnected as one set in a one-to-one relationship in such a manner as tobe capable of communicating with each other through the network 800. Thelaser machining units 10A-2 to 10A-n and corresponding ones of themachine learning units 700-2 to 700-n are connected in the same way asthe laser machining unit 10A-1 and the machine learning unit 700-1. InFIG. 12 , n sets of the laser machining units 10A-1 to 10A-n and themachine learning units 700-1 to 700-n are connected through the network800. Alternatively, each set of a laser machining unit and a machinelearning unit belonging to the n sets of the laser machining units 10A-1to 10A-n and the machine learning units 700-1 to 700-n may be connecteddirectly through a connection interface. Two or more of the n sets ofthe laser machining units 10A-1 to 10A-n and the machine learning units700-1 to 700-n may be installed in the same factory, or all these setsmay be installed in different factories, for example.

For example, the network 800 is a local area network (LAN) installed ina factory, the Internet, a public telephone network, or a combination ofthese networks. The network 800 is not particularly limited in terms ofa specific communication system, or whether the network 800 is connectedwith a wire or without wires, for example.

One machine learning unit and a plurality of laser machining units maybe connected directly or through a network in a manner allowingcommunication therebetween, and this machine learning unit may beresponsible for machine learning on these laser machining units. In thiscase, a distributed processing system may be employed to distribute thefunctions of the machine learning unit between a plurality of serversappropriately. Alternatively, the functions of the machine learning unitmay be realized using a virtual server function in a cloud, for example.

In the presence of a plurality of laser machines such as the one shownin FIGS. 1 and 2 and having the same model name, having the samespecification, or belonging to the same series, these laser machines maybe configured to share a learning result obtained by each of the lasermachines. This makes it possible to construct a model closer to anoptimum model.

<Online Learning, Batch Learning, and Mini-Batch Learning>

The foregoing supervised learning by the learning unit 730 may beperformed as online learning, batch learning, or mini-batch learning.The online learning is a learning method by which, each time the lasermachine is driven and training data is generated, the supervisedlearning is performed immediately. The batch learning is a learningmethod by which, while the laser machine is driven and training data isgenerated repeatedly, multiple pieces of training data responsive to therepetitions are collected and the supervised learning is performed usingall the collected pieces of training data. The mini-batch learning is alearning method intermediate between the online learning and the batchlearning by which, each time a certain quantity of training data isaccumulated, the supervised learning is performed.

The foregoing embodiment can be realized by hardware, software, or acombination of hardware and software. Being realized by software meansbeing realized by reading and execution of a program by a computer. Ifhardware is used for configuring the embodiment, each embodiment can beconfigured using an integrated circuit (IC) such as a large scaleintegrated circuit (LSI), an application specific integrated circuit(ASIC), a gate array, or a field programmable gate array (FPGA), forexample.

The foregoing embodiment can be configured partially or entirely using acombination of software and hardware in a computer including a storageunit such as a hard disk or a ROM storing a program describing all orpart of the operation of the machine learning device illustrated in theflowchart, a DRAM storing data required for operation, a CPU, and a busfor connection between the units. In this computer, the embodiment canbe configured by storing information necessary for the operation intothe DRAM and making the CPU execute the program.

The program can be stored into various types of computer-readable mediaand can be supplied to the computer. The computer-readable media includevarious types of tangible storage media. Examples of thecomputer-readable media include a magnetic storage medium (a hard diskdrive, for example), a magneto-optical storage medium (anmagneto-optical disk, for example), a CD read-only memory (CD-ROM), aCD-R, a CD-R/W, and a semiconductor memory (a mask ROM, a programmableROM (PROM), an erasable PROM (EPROM), a flash ROM, or a random accessmemory (RAM), for example).

EXPLANATION OF REFERENCE NUMERALS

10 Laser machine

20 Workpiece

30 Laser machining system

100 Control unit

101 Program analysis unit

102 Laser command output unit

103 Position command calculation unit

104 Head control unit

105 Compensated position command calculation unit

200 Laser oscillator

300 Scanner head

400 CCD camera

500 Temperature sensor

600 Coefficient calculation unit

700 Machine learning unit

L Laser beam

What is claimed is:
 1. A machine learning device that performs machinelearning on a laser machine comprising a plurality of galvanometermirrors for reflection of a laser beam and a plurality of galvanometermotors for driving and rotating corresponding ones of the galvanometermirrors according to driving control data generated based on a positioncommand indicating a machining target position on a workpiece, andscanning the laser beam over the workpiece, the machine learning devicecomprising: input data acquisition unit that acquires, as input data, adetected temperature of at least one galvanometer mirror of theplurality of galvanometer mirrors and a detected temperature of at leastone galvanometer motor of the plurality of galvanometer motors thatdrives and rotates the at least one galvanometer mirror; labelacquisition unit that acquires, as a label, a coefficient forcalculating the machining target position from an actual position ofmachining with the laser beam on the workpiece relative to the machiningtarget position, the coefficient being acquired from a coefficientcalculation unit before calculation of the machining target position andthe actual machining position, the coefficient calculation unitacquiring machining target position data at the time of acquisition ofthe detected temperature of the at least one galvanometer mirror and thedetected temperature of the at least one galvanometer motor as the inputdata by the input data acquisition unit, and concurrently obtaining thecoefficient by solving a plurality of mathematical expressions; andlearning unit that performs supervised learning using a set of the labeland the input data as training data to construct a mathematical modelfor calculating the machining target position from the actual machiningposition on the workpiece on the basis of the at least two detectedtemperatures consisting of the detected temperature of the at least onegalvanometer mirror and the detected temperature of the at least onegalvanometer motor, the learning unit calculating a plurality ofcoefficients defined based on the at least two detected temperaturesconsisting of the detected temperature of the at least one galvanometermirror and the detected temperature of the at least one galvanometermotor according to the mathematical model using the training data, andoutputting the mathematical model.
 2. The machine learning deviceaccording to claim 1, wherein the machining target position is outputfrom the laser machine, the actual machining position on the workpieceis output from position detection unit that detects the actual machiningposition relative to the machining target position on the workpiece, andthe coefficient is output from coefficient calculation unit thatcalculates the machining target position output from the laser machinefrom the actual machining position output from the position detectionunit.
 3. A laser machine comprising: the machine learning deviceaccording to claim 1; a scanner head comprising a plurality ofgalvanometer mirrors for reflection of a laser beam and a plurality ofgalvanometer motors for driving and rotating corresponding ones of thegalvanometer mirrors according to driving control data generated basedon a position command indicating a machining target position on aworkpiece, and scanning the laser beam over the workpiece; positioncommand calculation unit that calculates a machining target position onthe workpiece; position detection unit that detects an actual machiningposition relative to the machining target position on the workpiece;coefficient calculation unit that calculates a coefficient for amathematical model for calculating the machining target position to beoutput from the position command calculation unit, based on the actualmachining position output from the position detection unit; at least twotemperature detection units that detect a temperature of at least onegalvanometer mirror of the plurality of galvanometer mirrors and atemperature of at least one galvanometer motor of the plurality ofgalvanometer motors that drives and rotates the at least onegalvanometer mirror; and compensated position command calculation unitthat calculates a compensated machining target position for making amatch between the machining target position and the actual machiningposition from the machining target position on the workpiece at at leasttwo detected temperatures detected by the at least two temperaturedetection units and consisting of the temperature of the at least onegalvanometer mirror and the temperature of the at least one galvanometermotor, the compensated position command calculation unit calculating thecompensated machining target position by using a mathematical model forcalculating the machining target position from the actual machiningposition relative to the machining target position on the workpiece onthe basis of at least two detected temperatures output from the machinelearning device and consisting of the temperature of the at least onegalvanometer mirror and the temperature of the at least one galvanometermotor, the mathematical model being outputted from the machine learningdevice that performs supervised learning using training data includinginput data and a label, the input data containing the temperature of theat least one galvanometer mirror and the temperature of the at least onegalvanometer motor, the label being a coefficient for a mathematicalmodel for calculating the machining target position to be output fromthe position command calculation unit, based on the actual machiningposition calculated by the coefficient calculation unit at the time ofacquisition of the input data.
 4. A laser machine comprising: a scannerhead comprising a plurality of galvanometer mirrors for reflection of alaser beam and a plurality of galvanometer motors for driving androtating corresponding ones of the galvanometer mirrors according todriving control data generated based on a position command indicating amachining target position on a workpiece, and scanning the laser beamover the workpiece; position command calculation unit that calculates amachining target position on the workpiece; at least two temperaturedetection units that detect a temperature of at least one galvanometermirror of the plurality of galvanometer mirrors and a temperature ofleast one galvanometer motor of the plurality of galvanometer motorsthat drives and rotates the at least one galvanometer mirror; andcompensated position command calculation unit that calculates acompensated machining target position for making a match between themachining target position and an actual machining position from themachining target position on the workpiece at at least two detectedtemperatures detected by the at least two temperature detection unitsand consisting of the temperature of the least one galvanometer mirrorand the temperature of the at least one galvanometer motor, by using amathematical model acquired through supervised learning using input dataand a label, the input data containing at least two detectedtemperatures consisting of the temperature of the at least onegalvanometer mirror and the temperature of the at least one galvanometermotor, the label being a coefficient for calculating the machiningtarget position from the actual machining position relative to thetarget position of machining with the laser beam on the workpiece, thecoefficient being acquired from a coefficient calculation unit beforecalculation of the machining target position and the actual machiningposition, the coefficient calculation unit acquiring machining targetposition data at the time of acquisition of the temperature of the atleast one galvanometer mirror and the temperature of the at least onegalvanometer motor as the input data by the input data acquisition unit,and concurrently obtaining the coefficient by solving a plurality ofmathematical expressions.
 5. A laser machining system comprising themachine learning device according to claim 1 and a laser machine, thelaser machine comprising: a scanner head comprising a plurality ofgalvanometer mirrors for reflection of a laser beam and a plurality ofgalvanometer motors for driving and rotating corresponding ones of thegalvanometer mirrors according to driving control data generated basedon a position command indicating a machining target position on aworkpiece, and scanning the laser beam over the workpiece; positioncommand calculation unit that calculates a machining target position onthe workpiece; position detection unit that detects an actual machiningposition relative to the machining target position on the workpiece;coefficient calculation unit that calculates a coefficient for amathematical model for calculating the machining target position to beoutput from the position command calculation unit, based on the actualmachining position output from the position detection unit; at least twotemperature detection units that detect a temperature of at least onegalvanometer mirror of the plurality of galvanometer mirrors and atemperature of at least one galvanometer motor of the plurality ofgalvanometer motors that drives and rotates the at least onegalvanometer mirror; and compensated position command calculation unitthat calculates a compensated machining target position for making amatch between the machining target position and the actual machiningposition from the machining target position on the workpiece at at leasttwo detected temperatures detected by the at least two temperaturedetection units and consisting of the temperature of the at least onegalvanometer mirror and the temperature of the at least one galvanometermotor, the compensated position command calculation unit calculating thecompensated machining target position by using a mathematical model forcalculating the machining target position from the actual machiningposition relative to the machining target position on the workpiece onthe basis of at least two detected temperatures output from the machinelearning device and consisting of the temperature of the at least onegalvanometer mirror and the temperature of the at least one galvanometermotor, the mathematical model being outputted from the machine learningdevice that performs supervised learning using training data includinginput data and a label, the input data containing the temperature of theat least one galvanometer mirror and the temperature of the at least onegalvanometer motor, the label being a coefficient for a mathematicalmodel for calculating the machining target position to be output fromthe position command calculation unit, based on the actual machiningposition calculated by the coefficient calculation unit at the time ofacquisition of the input data.
 6. The laser machining system accordingto claim 5, wherein the machine learning device and the laser machineare connected through a network.